Predictive Modeling of Epidemics using Big Data

TITLE	Predictive Modeling of Epidemics using Big Data
ABSTRACT	The healthcare industry is experiencing a digital revolution fueled by the exponential growth of big data and the integration of artificial intelligence (AI). Massive volumes of heterogeneous data are generated daily from electronic health records, medical imaging, genomics, wearable devices, and telemedicine platforms. While this data presents unprecedented opportunities for advancing personalized medicine, predictive analytics, and operational efficiency, its fragmented nature, lack of interoperability, and strict privacy regulations pose significant challenges. Big data analytics, supported by AI techniques such as machine learning, natural language processing, and predictive modeling, offers powerful tools to extract actionable insights from both structured and unstructured datasets. These capabilities enable early disease detection, outcome prediction, optimized resource allocation, and enhanced clinical decision-making. However, critical issues such as data security, algorithmic bias, ethical concerns, and unequal access to digital healthcare resources must be addressed to ensure responsible and equitable adoption. The COVID-19 pandemic highlighted both the potential and the limitations of big data-driven healthcare systems, underscoring the urgency of developing secure, inclusive, and transparent frameworks. This paper explores the role of big data and AI in transforming healthcare, identifies existing gaps, and proposes strategies for leveraging advanced analytics to achieve improved patient outcomes, operational sustainability, and global health equity.
AUTHOR	MG Mamatha, Manini Medha, Madan Kumar D Department of Computer Applications, CMR Institute of Technology, Bengaluru, India
PUBLICATION DATE	2025-09-09
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1204081
PDF	81_Predictive Modeling of Epidemics using Big Data.pdf
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